Evolving Multiple Agents by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InCollection{iba:1999:aigp3,
  author =       "Hitoshi Iba",
  title =        "Evolving Multiple Agents by Genetic Programming",
  booktitle =    "Advances in Genetic Programming 3",
  publisher =    "MIT Press",
  year =         "1999",
  editor =       "Lee Spector and William B. Langdon and 
                 Una-May O'Reilly and Peter J. Angeline",
  chapter =      "19",
  pages =        "447--466",
  address =      "Cambridge, MA, USA",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, QGP",
  ISBN =         "0-262-19423-6",
  URL =          "http://www.cs.bham.ac.uk/~wbl/aigp3/ch19.pdf",
  abstract =     "On the emergence of the cooperative behaviour for
                 multiple agents by means of Genetic Programming (GP).
                 Our experimental domains are multi-agent test beds,
                 i.e., the robot navigation task and the Tile World. The
                 world consists of a simulated robot agent and a
                 simulated environment which is both dynamic and
                 unpredictable. In our previous paper, we proposed three
                 types of strategies, i.e, homogeneous breeding,
                 heterogeneous breeding, and co-evolutionary breeding,
                 for the purpose of evolving the cooperative behavior.
                 We use the heterogeneous breeding in this paper. The
                 previous Q-learning approach commonly used for the
                 multi-agent task has the difficulty with the
                 combinatorial explosion for many agents. This is
                 because the state space for Q-table is so huge for the
                 practical computer resources. We show how successfully
                 GP-based multi-agent learning is applied to multi-agent
                 tasks and compare the performance with Q-learning by
                 experiments. Thereafter, we conduct experiments with
                 the evolution of the communicating agents. The
                 communication is an essential factor for the emergence
                 of cooperation. This is because a collaborative agent
                 must be able to handle situations in which conflicts
                 arise and must be capable of negotiating with other
                 agents to reach an agreement. The effectiveness of the
                 emergent communication is empirically shown in terms of
                 the robustness of generated GP programs.",
  notes =        "AiGP3",
}

Genetic Programming entries for Hitoshi Iba

Citations